Posted
by
timothy
on Sunday June 24, 2012 @06:18PM
from the use-lots-of-food-and-rent-analogies dept.

An anonymous reader writes "As a calculus professor for a small undergraduate institution, I normally lecture students who are majoring in the natural (or 'hard') sciences, such as mathematics, physics, and computer science. In fact, I have done so for almost thirteen years. However, for the first time this fall semester, we have a shortage of professors on our hands. As a result of this, I have been asked to teach a general education statistics class. Such classes are a major requirement for the large psychology student body we have here. I have never lectured social science students in any mathematics-related classes. My question to the Slashdot community is as follows: What are your experiences with teaching natural science classes to social science students? How is the experience the same or different in comparison to natural science students who may be more adept to the nuances of mathematics and other similar fields?"

I have to somewhat disagree with this. In teaching my inorganic chemistry and organic chemistry students, there is a huge difference between the chem majors and the biology majors (in genchem, they are still pretty much the same, as they haven't been "indoctrinated" yet). The chem majors know that do well, they need to practice, practice, practice. The biology students are all about memorization, flash cards, that kind of thing. Most catch on by the end, but for psych students, I strongly suggest that you drill into them from the very first day that the only way they will succeed in the class is by doing practice problems every day.

What? Sure you're right about the galaxy and universe, but the sun is most certainly the center of the solar system for almost all practical purposes. Even if you want to be a stickler about it and call the solar system's barycenter the center, the sun is rarely further than one radius away, and only one of them can you point at on a moments notice. http://en.wikipedia.org/wiki/File:Solar_system_barycenter.svg [wikipedia.org] Next you'll be claiming the axle isn't actually the center of a wheel because the tire flexes a

This gets label "insightful"? Here's another: the sun rises in the east. Jeez.From my personal experience, teaching both science and Psy students, I can tell you there's quite the gap. Like the 3rd year student that asked: What's a square root?

...especially as regards the use of mathematics in the interpretation of 'data' where the soft sciences have such a 'hand wavy' approach to cause and effect.

To me, economics is a prime example. Forgive me if I'm off base in in my belief that economics is both sociological and soft(headed), but tyring to measure human behavior in the absence of an accounting for political corruption within this purely human realm and leaving the so-called black market beyond it's consideration leaves the inclusion of economics within the realm of 'science' suspect.

I would haved greatly appreciated any attempt by a professor to explain the difference between soft science and hard science, especially if it included an math based explanation of the nuance between these different domains.

Soft sciences are typically about trying to solve 'wicked' [wikipedia.org] problems, which are those that are generally impossible to completely solve (end poverty or health inequality, understand crime, migration, or human behaviour in general etc). Hard scientists typically try to solve problems that are relatively much easier because they have a simple concrete goal (put a man on the moon, make a bomb, cure some disease)

Soft scientists need a much stronger theoretical framework to interpret their data, because of the absence of any really testable mechanisms for the effects they observe. This can come across as 'hand wavy' but it really isn't. Your economics example isn't entirely fair, some economic models will include corruption and black markets etc and others wont, just as some physics models include relativistic effects and others don't. A good scientist has to choose the right model to approah any problem, regardless of discipline.

I've been working in an inter-disciplinary group and have had the opportunity to see medics and economists try to work together. The two cultures are very different in their scientific approach, both consider the other to be unnecessarily picky about some aspects of the work while not being rigorous enough in others. Eg economists spend a huge amount of their time trying to prove causation in observational data, while medics will typically wave this away if they think the causal effect is likely enough. On the other hand economists tend not to contextualise their results well enough, while medics will see the bigger picture in terms of building on existing science.

No. Soft sciences need the same rigorous theoretical framework as everybody else. It's just that practitioners don't know how to use the theory correctly. Researchers in the hard sciences are just as guilty.

I will try to inform you a little about economics (speaking as the holder of both a BSc and PhD in Economics):

The key difference is that economics and social sciences are mostly non-experimental (people don't take kindly to you arbitrarily changing their parents, education, or wealth - which is the 'experimental' way of establishing cause and effect). This means that the statistical issues are orders of magnitude larger than those that exist in experimental sciences. In an experimental science you can go off and get new data where you have controlled for most everything except the effect you are interested in and a simple regression will generally be all you need. In a non-experimental science you are stuck with the data that nature has given you. As a result you need to be very careful to get meaningful results. But, in case you are doubting, you can get meaningful results if you are careful enough.

Thus, my second point: Economics is not soft headed. In fact, it is very hard headed because you need to be when you are dealing with data that are generally speaking - crap. There are so many ways you can be mislead by non-experimental data and you need to be very hard-headed to avoid this. I won't claim mistakes haven't been made, but those mistakes are the reason economics has gotten much better at dealing with this than many people might realise. But, there is only so much you can do when the data are the way they are.

So don't assume the difficulty of getting solid results in economics reflects the ability of the practitioners rather than the raw materials you are dealing with.

Avoid using overly abstract concepts, and try to put things in terms they can understand. Since you are teaching statistics, try to use a lot of gambling references (lotto, roulette, etc.) since nearly all the students will have some familiarity with those.

I've found I can teach engineering concepts to elementary school teachers as long as I avoid formulae (and avoid using Latin references, so use the term "formulas":-) ).

Avoid using overly abstract concepts, and try to put things in terms they can understand.

Arts major here, who's been working for about 20 years in IT. I'd offer a qualified agreement here. I found some science subjects innately easy, because I was able to visualise the forces at work. Vector equations in physics, geometry, etc. were dead easy, even when they became more advanced. But the moment the teacher began to fall back on jargon and symbolic shorthand, I'd get lost.

The reason is pretty straightforward. I am extremely good at certain kinds of pattern-identification, but quite poor at others. Among the ones I'm poor at are mathematical equations, which are not evaluated in the same way natural languages are. It's merely a left brain/right brain thing, and I can compensate by using different approaches. I thrived under teachers who understood this, and died under teachers who spent their entire time writing equations on the board without attempting to contextualise them.

I think some of it is getting the big picture / motivation as well. A lot of students don't have the background many Slashdotters have in documentaries, natural-science museums, even sci-fi, which can lay the big-picture groundwork, with which you can then dive right into equations and methods in the courses. When it comes to physics, for example, a large number of students probably first need to be brought up to "read some Carl Sagan" levels of understanding, which would put them in a lot better position to learn more quantitative aspects.

One book you should check out is Larry Gonick, Cartoon Guide to Statistics. I taught statistics to general ed students eons ago, and I found the textbooks uniformly execrable. recently, I had a couple of pals who had been forced twice to drop business stats, which were essential to getting their BBA degrees. I suggested the Gonick book, which I had recently found. One guy got a B the other, a C. it is a superb intro, largely due to the cartoon aspect.

A great deal of math and science is conceptually trivial. What trips me up is symbolic notation. For some reason, it gives my brain fits. Give me the same problem with a decent verbal explanation and yeah, I'll have it coded up for you in a few minutes, thanks. Obviously math notation works for most people, but not for everyone.

Perhaps that explains how come I blitzed physics, understood math reasoning (and I like math)... but was utterly lost with math proofs, which meant I failed math and thus wasn't able to continue with physics.

I just assumed that I am math stupid, but perhaps it was only the wrong teaching method for me. Thanks.:)

Good advice. One nitpick, though. I teach statistics to non-STEM majors, and many aren't impressed with my examples from gambling and games. I use them regardless, but not as much as other examples they find more interesting.

Leave it to slashdot to invalidate the law by using a question mark for something that's not even a question!:)

In any case, I think the law only applies to questions that have yes/no answers, and not, for example, to questions like "Who Stole the Mona Lisa?", "What is the Effect of Coffee on Sleeplessness?", "Where Will the Enema Bandit Strike Next?", "When Will the Bridge Re-Open?" or "Why Can't Johnny Read?"

Isn't the use of statistics pretty much the only thing that distinguishes 'psychology' from 'talking about feelings'?

I realize that most psych majors don't actually go on to practice in psychology or psychiatry, and the ones that do generally have to do some flavor of graduate work; but I'm still rather alarmed by the implication of TFS that psych students might well be deeply uncomfortable with statistics...

Ironically, I've found many computer science majors are not very versed in the ramification of statistics either. I think it has something to do with the binary world that they envisage or something like that.

The most common example is the "1-in-a-million" mentatlity many computer science majors have when talking about bugs or special-case code paths. You'd think they'd know better as they can often quote all sorts of statistical sort or database traversal, O(log n), big-o little-o, etc, but when you get

It certainly isn't a virtue in either case; but I think that I'd actually be more comfortable with the computer science major who lacks a grasp of statistics. There are perfectly reputable areas of mathematics that fall under 'computer science' and don't involve statistics. In an empirical, largely study-based, subject like psychology, though, if you can't use statistics you are essentially stuck at the 'anecdote' stage of knowledge...

Ironically, I've found many computer science majors are not very versed in the ramification of statistics either. I think it has something to do with the binary world that they envisage or something like that.

The most common example is the "1-in-a-million" mentatlity many computer science majors have when talking about bugs or special-case code paths. You'd think they'd know better as they can often quote all sorts of statistical sort or database traversal, O(log n), big-o little-o, etc, but when you get them with a common sense thing about code performance issue, they appear to get some sort of temporary lobotomy.

As someone who's been on both of those academic sides (I started in hard, and moved into soft four years later), I never thought it was a lack of comprehension when fellow students have trouble with hard sciences. Instead, it's an appreciation for numerical conclusions.

Hard sciences basically tend to conclude three steps earlier than soft sciences -- because the math ends there. Hard sciences tend to describe a scenario, detail it numerically, hypothesize a numerical result, experiment numerically, solve for x, and x=n is the answer. The issue for soft science students is really that nobody ever cared about x. Hard sciences very quickly forget where x came from, because the entire scenario was translated into numbers. This affords hard sciences a certain level of abstraction, making problems faster to solve, easier to solve, and more widely relevant to re-apply.

Soft sciences tend to be industries where some aspect of the scenario can't be translated into numbers. It's usually a black-box scenario, and psychology is a good example. Such experiments don't attempt to describe certain behavioural anomalies numerically. Instead, 40% - 80% of a scenario is translated into numbers, leaving the remaining 20% - 60% as mysterious elements. Imagine a hard science equasion where six linear constants simply cannot be merged into a single constant -- for no seemingly good reason. As a direct result, after solving for x, the numerical abstraction must then be de-abstracted back into whatever the real-world scenario actually is. This procedure is not only an effort to grasp, but it's also a a major point of interpretation at the end of an experiment -- usually because x isn't the number of grams diluted; instead x is the likelihood that a person might turn left.

The nice part about de-abstracting at the end is that you wind up with a real-world answer, not a mystery number.

So my point is, that for a social science student used to walking in with a scenario, and walking out with a conclusion, you need to teach them how to appreciate the hard-science "datum result" without having a one-question-one-answer conclusion.

You can see this same effect in the business world. Big business corporate C.E.O.'s often make decisions from numbers in, to predict numbers out, without ever knowing where the numbers came from, nor how they'll be used on the way out. But if you've seen anyone go through "board of director" training, you know that the skills wind up applying to any business anywhere because they are all done at the hard-science executive level.

Constrast that to the entrepreneur of a small business, who needs to make all of the same decisions, but simply doesn't have the sample-size of data coming in to ever be able to make decisions numerically like the corporate guy -- which is one of the primary reasons that he has an advisory board instead of a board of directors. The decision-making process is very different, even though they are the same questions and the same answers. And each has a very difficult time in the other's business world.

Here's hoping someone else's response details a good way to actually teach that appreciation.

I may be the only mathematician who had this problem (I wasn't all that good) but Statistics threw me for a loop at
first (I was briefly fairly competent eventually). Statistics isn't calculus; calculus is a big part of classical statistics.

A pure mathematician hitting statistics cold may have almost as big a problem a student with little mathematics.
Mathematics knowledge can actually get in the way at first.

First thing to do is to get emacs and get the doctor watson mode working. Then have some sessions with Watson and understand how to talk to psychology students. To my best understanding, it involves rephrasing their questions and asking them why they ask that question or what their feeling is. All you need to do is to wing it for 50 minutes and charge them one hour of tuition fees. They will get the hang of it and learn to speak to their clients for 50 minutes and bill them for an hour.

First thing to do is to get emacs and get the doctor watson mode working.

Doctor mode (M-x doctor). I'm not sure what a "doctor watson" mode would be. Would it be like the old movie version? Follow you around and act dumb to try to make you seem smarter, and occasionally exclaim, "that's simply astounding!", and "I don't know how you do it"?

I must have confused the M-x doctor mode of emacs with the Dr Watson error dialogs from Windows NT. Sorry for the mistake. I have not used a true emacs editor for a long time. (no, no I did not switch to vi, nor to MsDev editor. I am using Visual Slick Edit that supports all the key binding of Emacs).

I'm not sure what a "doctor watson" mode would be. Would it be like the old movie version?

I would register for that, or probably any class taught as if it were an old movie, preferably in a 1920s or 1930s style, or like a Three Stooges flick. "You's guys gotta buckle down, see, and cut all the hubbub. HEY wise guy! Pay attention in the back, you numskulls, before I get sore at ya's, see. Why I oughta..."

On second thought, maybe I will attempt to lecture in character. It will bring an added sense of focus for both I and the students, or something. Hmm...

You make it sound like you are teaching physics to special ed classes.

They are as smart as everyone you've had so far. You may see some differences in their backgrounds, but that's easy enough if you make allowances to give more basics or point them to appropriate resources. I'd give an example, but I have no idea what "natural science" is to you. Geology and oceanography are natural sciences, same as physics, but they share little in common.

One thing you may notice is that arts students in hard classes may want more "why" than "how" answers. So be prepared for more philosophical discussions, or correct, if silly, comments (i.e., the "why" for valence electrons is that the stable ones are like a comforable couch, and the unstable ones are hard benches. You want the better seat, but you don't really want to get up, and the worse the chair, the sooner you'd move) or something like that. The "why" as an expression of potential energy in MeV won't get the point across as well as a discussion of musical couches, and they'll remember it better, isn't that the goal, over the goal of the hard science students where accuracy is above all.

I wanted to mod you up, but I'd rather add this: Psych students need to know statistics. Statistical analysis is 90% if their later term research; my sister spends paper writing time compiling data on people, analyzing how patterns stack up into behavior predictions. Yes, you can look at a group of people and predict what the chance is that one will have a mental illness, or who is suicidal, etc. It's not soft science, it's actuarial math. Combining individual research into meta-analysis to see how certain medications affect both groups and individuals. Seeing how changes in groups affect individual members. Even at the undergrad intern level, that was what her last two years of psychology classes were.

My advice to the poster is simple. They aren't idiots, and they need to know how stats work. Don't start classes with intense set-theory notation unless they have that as a pre-req. Don't pull a Taylor series out to explain something if the school doesn't require a course with that as a pre-req. Use lots of people examples, instead of abstract "X is a part of set S"; and as someone else suggested gambling stats are also good. And for their sake, don't talk down to them unless you want them to fail. Or if you have tenure. These are psych students, they can manipulate the hell out of you if you seem to be annoyed with them.

Note: if you pace from one side of the lecture hall/room to the other a lot, watch for them to drop papers and pencils when you do. Classic psych prank to get a teach to stop pacing. They can have you trained by the end of a semester if they want.

Step one when teaching a class like this: ask the department that they are in what these students will need to know and why it is a required class.

When I was forced to teach introductory logic to mathematics majors, that is what I did. Not only did it make my examples something they were more familiar with, but it also caused me to change my curricula that I offered by skipping certain things they didn't need (e.g. square of opposition) and focusing more on what they do need (e.g. WFFs and formal systems)

If I had a dollar for every paper published in a peer-reviewed social sciences journal which totally abused statistics, I'd retire and use my extra cash to fund organizations directed at basic logic and math education, trying to help with the situation.

Most social studies students I knew had little understanding of the statistics they were using. It was basically a magic incantation for giving them results and making their conclusions sound more credible to other people who likewise didn't understand statistics. The result is bad statistics and bad science. Yes, these people aren't idiots, but they've become used to being rewarded without having to think rigorously.

The impression I get is that the pattern persists even among those few who make it into the field. There are some psychologists etc who are really trying to do real science- a difficult task since the basic concepts are even more up in the air than the basic concepts of chemistry were in the days of the alchemists. As far as I can tell, however, quite a lot are quite happy to be able to find ways of running a study so it will inevitably vindicate their preexisting biases and will fudge the statistics to match.

For the OP: You're right to be concerned. Students for the GE stats class are usually woefully underprepared. Rather than giving them the rigorous preparation in logic, multivariate calculus, etc they really need to understand statistics, the GE stats class does the equivalent of the Wizard's favor to the Scarecrow.

"I can't give you a brain, so I'll give you a passing grade! Now you understand statistics! Go back to your department now, please. (Phew, they're gone at last. That kind of work may pay the bills here in the Stats dept. but it doesn't do wonders for my sense of academic integrity as an educator.)"

If I had a dollar for every paper published in a peer-reviewed social sciences journal which totally abused statistics, I'd retire and use my extra cash to fund organizations directed at basic logic and math education, trying to help with the situation.

What is your sample size for this decision; and psychologically is there any cost to you for papers where it is applied correctly? What is your confidence value? How varied is your sample; all from the school(s) you attended, or from multiple? Self selected and memorable misuses in articles, or actual comparisons, or even meta-analysis of other papers?

Oh, you didn't apply statistics to your complaint about misuse of statistics? Consider yourself the first stat in my new study "People who whing about statis

If I had a dollar for every paper published in a peer-reviewed social sciences journal which totally abused statistics, I'd retire and use my extra cash to fund organizations directed at basic logic and math education, trying to help with the situation.

Sadly that's true of the hard sciences, too.

Most social studies students I knew had little understanding of the statistics they were using. It was basically a magic incantation for giving them results and making their conclusions sound more credible to other people who likewise didn't understand statistics. The result is bad statistics and bad science. Yes, these people aren't idiots, but they've become used to being rewarded without having to think rigorously.

That's a remarkably naive view of the social sciences. There is absolutely no academic field in a responsible academic institution in which practitioners are "rewarded without having to think rigorously". The difference is in what they think rigorously about. Consider, for example, child development. The hard sciences will think rigorously about the factors that affect the rate of child development, the social sciences will think rigorously about how unusual rates

A major problem with these sorts of courses is that they're often not taught in a way that emphasizes their utility to the student. If you're thinking about being a psychologist for example why is calculus important? I'm not saying it isn't. I can think of several different ways it could be very important especially as it regards understanding statistics.

But you might want to create some test questions that relate to their majors.

In business calculus they focus on it's relationship to various economic calculations. So you might want to look at drug trial statistics or anthropological/demographic statistics.

And for the love of God... please tell them that correlation is not causation. You'd be doing everyone a huge favor. These guys are going going write stupid papers or write blogs or something similar that will pop up in the media. And everyone here at slashdot will be facepalming over another dumb paper that didn't acknowledge that simple fact.

Always interesting to see the categories different parts of academia place each other in. The post's author is calling math, physics and comp-sci "natural sciences" and apparently considers statistics to be "social science". I'm a geology professor and, as far as I'm aware, my colleagues and I tend to consider Earth, environmental, and biological sciences to be the "natural sciences"; physics, chemistry, engineering, and any math to be "physical science"; and psychology, sociology, (cultural) anthropology, etc. to be "social sciences". Everything else is art and/or humanities.

I wonder how other groups categorize one another? Right off the bat I'd suspect that mathematicians don't always consider themselves scientists. Perhaps ditto for engineers. People tend to form and place each other in groups of varying degrees of subjectivity. How you place others probably says something about the standards and values of one's own group.

This sounds like it'd make a great piece of social-psych research! They love this kind of fluff, right? (j/k)

The post's author is calling math, physics and comp-sci "natural sciences" and apparently considers statistics to be "social science". I'm a geology professor and, as far as I'm aware, my colleagues and I tend to consider Earth, environmental, and biological sciences to be the "natural sciences"; physics, chemistry, engineering, and any math to be "physical science"; and psychology, sociology, (cultural) anthropology, etc. to be "social sciences".

My mental image of natural science also includes biology, geology and ecology in the "natural sciences". I'd consider maths and comp-sci to be too abstract to be a natural science (something like the study of patterns and algorithms rather than the observation of patterns and algorithms).

Always interesting to see the categories different parts of academia place each other in. The post's author is calling math, physics and comp-sci "natural sciences" and apparently considers statistics to be "social science".

I think you misread. He's not calling statistics a social science. He's asking how to teach "hard" math (statistics) to students whose background is in "soft" social sciences.

I don’t mean for this to sound arrogant, but it probably will. I was a physics major who took a statistics course that was taught in the Psychology Department and meant for psychology students. A lot of science and math majors took the course as a way to pad their GPA’s. I could see from the books the other students brought to class that about one forth of the students were science or math majors. I think I made about a 96 on the first test and was embarrassed at the thing I missed. The cla

I don't see any mistake in his observation. I had a similar experience in a Logic course that was cross listed in Philosophy and Comp Sci.; the semester I took it the course was taught by an engineering professor who stated that, as in all engineering courses, the average grade for the class would be a C+. About 1/3 of the students immediately got up and walked out.

Clearly about 2/3 of the students were happy to remain enrolled in which their grades, upon which many real and important things (graduation, honors, finances) heavily depend, would be subject to an arbitrary and capricious curve.

I'd say your school must have had fairly lax admission requirements, since only 1/3 of your statistics class was smart enough to get out of a bad idea while the consequences were minimal.

I'm a physics professor -- aka, The Reason You Take Calculus -- and I totally agree. I think all high schools should teach statistics as a mandatory 12th grade math class. Students who intend to go into technical fields (physics, chemistry, carpentry, metalworking) should take trigonometry, and nobody should take pre-calculus or calculus until college.

I've always emphasized to my calculus students that while calculus grew out of physics originally, from 1830-50 the mathematicians threw out that derivation and rebuilt it from the ground up... so it would be useful for things other than physics:^) Absent calculus, you can teach discrete probability; you can teach descriptive statistics for a sample; but you can't teach them continuous distributions and all that goes with that, other than cookbook approaches. OTOH, anyone who takes only one probability

You might want to try this in a new way? Have your students use the Khan Academy to look at topic lectures. Take the short tests after each section to see who's having problems and with what sections. This allow you to provide the interesting stuff, make you lectures about the relevance of what they're learning to the process of understanding the flow and function of populations and how statistics are a powerful tool to let us begin to extract patterns of form and function inside what would would otherwise

At my univ, "stats" was a very core part of post-grad psychology. Unfortunately, many students only cared about stats with respect to surveys/questionnaires, and they had problems with that:(

BUT multivariate stats was still seen as important and was required, along with experimental design. At the undergrad level the psycho-stats included the basics, including null-hypothesis, which stats to use depending on the experimental design etc.

First, on any engineering courses the students take for granted the need for math/science. That's not your case, so take some time every class to explain why and how this could be useful for your students beyond passing the grade

Second, they usually had a very hard time with school math, so take it easy and by all means try to avoid showing how smart you are when dealing with the abstractions and the logic, instead focusing on how little is needed to cover most of your material.

If you're not familiar with it, I recommend you read Ken Bain's What the Best College Teachers Do (2004) [hetl.org] which provides a wide range of insights and approaches that can help you out in any classroom. Speaking as a former science major who went on to a Ph.D. in history, the number one difference I notice between the streams is that many of the social science and humanities students believe they're bad at math and statistics. Problems in high school convinced them that they can't cut it - a high proportion will claim they're incapable in the fields. The secret to your success is convincing them that they can and want to master these skills.

I know - I teach a stats module as part of my sophomore course for majors. They learn how to read, interpret and critique statistics in articles in their field of study. Did you know that most of them don't know how to read and interpret statistics? The number of students at the start of the course who tell me they don't stop to read the charts because "they'll never understand them" is staggering. Statistical literacy should be the bedrock skill you inculcate. Show them good and bad uses of statistics. Teach them to figure out when someone's playing fast and loose with figures, hoping to fool readers. That will build their confidence and their thirst for knowledge.

My students go on to create their own time series and other statistical outputs from a dataset that they all find fascinating. (I use the Old Bailey Online [oldbaileyonline.org] for this, a website with material in statistically manipulable format for almost 200,000 trials at London's major criminal court: almost everyone finds the history of crime at least a little bit intriguing and so they will persevere a bit more when they run up against problems or road blocks.) Don't waste a lot of the time throwing new theories at them - make sure that every new concept you introduce is tied to something they'll want to and be able to explore.

Sure, some won't want to try. They'll find the work too hard or uninteresting no matter what you do. But others will be able to master this if you make it clear both why they need to learn certain techniques and how while giving them some clear and jargon-free walk-throughs. Exercises they can tackle tied into the fields they already find interesting are a great way to keep them motivated.

Look at some of the textbooks that are out there for stats that are directed to your U's social science fields - see what elements they emphasize as important for the field of psych, poli sci, etc., and then decide how you want to incorporate those key elements into your own teaching. Avoid getting too tied into teaching a particular software package - make sure they understand how to generalize their application.

Good luck - you're tackling what many consider a thankless course but one which can help to change students from math-phobic and fearful to at least statistically literate and confident that they can understand and apply some basic skills in the field as they go on in life.

I have to agree with this very strongly. Many people could do quite well in math, but got tripped up by one or two bad classes (or bad years) along the way, and if there is one thing that really separates the natural sciences from the other fields, it's the nested skills sets and dependencies on a ladder of skills - so if you miss a rung, you don't easily move up. Because higher education in the sciences and engineering is competitive - based more on regulating access to high-paying careers than on really d

First, it might not be important, but the title bugs me: statistics isn't a natural science.

I teach economics, and the biggest thing I note about my students is the heterogeneity in mathematical capabilities. I always need to keep on my toes about who I'm boring because they can handle that math in their sleep and who I'm leaving in the dust so that they're not even close to learning what I'm talking about. In a hard science program, there will presumably be some of that, but a bit more pressure on the low

I'm a physics professor who teaches some similar classes, including a course on climate change for nonmajors. I also deal with a lot of students who take stats. Statistics is probably the most uniformly loathed class in every university. Neither its students nor its professors want to be there.

Your first job is to convince students that they need to know this material, not just because it's a requirement but because it's vital. Start your class off with some statistical disasters. Drugs that were appro

I taught stat to a business school audience, too many years ago to think about. One thing you have to figure out is what to cover and from what viewpoint. Math students might be interested in the math behind some of the statistical methods. Social science students probably aren't. To be honest, they're just going to use canned packages, so details of the math are not the most important thing to teach them. What you really have to teach them is what all the math means. What assumptions are the methods based

The main thing you need to be aware of is that there are students in college -- decent numbers of them -- who cannot comprehend 7th grade math.

Not all social science majors are like this by any means. But there are some. They tend not to end up in the hard sciences, because they just won't survive there. But they can survive in other fields. What's more, they have the idea that it's OK not to understand math, and that it's "unfair" to demand that they have any kind of grasp of 7th grade math. I suspect

I've got an engineering background and have taught computer aided design and programming in the past. I've taught statistics to classes largely composed of psychology students a few times as well.

Know what they are expected to know: the prerequisites for the course I've taught are very minimal so I can fully expect some students to struggle with basic algebra. While the majority do seem to be able to 'plug and chug' reasonably well, their ability to actually understand what the equations they're using mean

Lots of bad advice in this thread. As a fellow mathematician who has taught intro stats before, and am currently teaching it (at a large research university) again this summer, here is my take:
1) Be prepared for the fact that many will not have taken a math class in many years, some 5 or more. They will recall little from their previous math classes other than intuition. Their arithmetic skills are poor. Be sure you are evaluating them on their understanding of the stats material, and be forgiving of

True Story: -Engineers at my school had to take 15 units of Arts courses as part of their studies, and Economics 100 was one of the popular choices. We had an Econ 100 class that matched a hole in the 3rd year mech and EE schedule, as a result we had about 2/3 engineers and 1/3 Arts students.

One day the prof, a very smart man with a subtle sense of humor, drew a graph of some function on the board. He drew the x and y axes, a straight line with a 45 degree slope and labelled the x intercept "a" and the y intercept "b". One of the girls from Arts puts up her hand and says "I don't think it should be that steep". The prof erases the line, redraws it half as steep and labels the x intercept "a" and the y intercept "b". "How's that" he says."Much better" says Arts girl.

Every engineer in the place falls over laughing. We laugh even harder as we see the confused look on Arts girl's face as she tries to figure out what's so damned funny. The prof never cracks a smile.

I'm sorry, but I think you should grow a little thicker skin. There's another interpretation to this joke: the arts girl knew exactly what she was doing, liked a less steep line better aesthetically, and was confused by the engineers' lack of artistic concern. The professor knew precisely what was happening (being an art person himself) which is why he didn't crack a smile. There, dumb art girl joke turned into dumb smug engineer joke.

The big cosmic joke that you haven't gotten yet is that you and the engineers looked down upon the girl never realizing that your arrogance and myopia prevent you from realizing your lack of any esthetic value which prevented you from seeing just how horrible that line really was.

You are teaching them stats, not calculus, so you shouldn't approach it like it is calculus. Social Science majors know they need statistical analysis. So do business majors.

For the record, my son had a major in psychology and a minor in math. Soft sciences and hard sciences are mutually exclusive and people don't go into the soft sciences because they can't do hard science. People go into soft sciences for the same reason as people go into hard sciences -- because they are interested in the subject matt

My favorite class to teach for the last 8 years or so has been sophomore-level statistics to psychology, sociology, occupational therapy, physician assistant (etc.) students at a CUNY community college. Statistics is directly and immediately applicable to those fields. (I have a research psychologist friend who says "all I do all day long is regression, regression, regression"). So I find a great thirst and relief that this may be the first math class these students have seen that's actually crystal-clearly

I've been teaching freshman-level physics, both algebra and calculus based, for about 15 years. My take (warning: generalities and averages ahead):

Coming into the class, the algebra students absolutely do not care about the theory of the subject. They do not see the beauty of the subject the way that you and I do, or that (to a lesser extend) the calculus-based students do. They have two goals: 1) They want to pass the class, because it is required for their major; and 2) they want to learn the material as

On the other end, my brother and ex-girlfriend were each psych majors in college and had to go through stats. Both failed it the first time (different schools, both had very large failure fractions in those classes). My brother in particular hated the course, though the second time he got a B without having the foggiest idea of what standard deviation means--I'm pretty sure they just used a massive curve because too many people were failing. Both of them embody math phobia. My ex eventually learned somethin

Stephen Greenfield, the best professor I ever had, happened to be one who mainly taught undergraduate math to math, physics, and engineering students and graduate math. However, he had a passion for teaching unlike I'd ever seen and he worked on a course at Rutgers [rutgers.edu] to teach math to non-sciency types.

The last paragraph on this page [rutgers.edu] has a description of the course.

I took a statistics course as an undergraduate and a stochastics course in a graduate EE curriculum. Despite the fact that the undergrad course was taught by a guy with a masters in mathematics, the two courses had NOTHING in common.

I'd advise dumbing the math way down. Present it as formulas to be used and teach students how to plug values from story problems into the formulas. Focus on why and how this is useful instead of on how it works.

Let me just say that psychology students are as bright as an university students, though they may be a bit different than the ones you are used to. My best advice: get ready for your office hours to be VERY busy. You will have a bit of learning on the job to do, you may not connect with your audience as well as you'd like at first (due in part to a different type of student, as well as how the course applies to their needs, more so than your abilities), and serious psych students are not bashful about showi

I taught Introductory Astronomy to a bunch of non-science majors looking to fulfill their science requirement. It was fun, that the kids were good at it despite lacking the physics and mathematics background for it. Statistics maybe isn't as interesting as astronomy, so keeping them interested is probably your biggest challenge. That would be true no matter who was taking the class.

Teaching to science and engineering students too often results in off topic discussions which put me off my lecture schedule (that's my problem, but it makes those classes more stressful to teach). Enthusiasm and detail are good, but lectures have a time limit. Pre-meds (a totally different category from science and engineering students) rarely show more than a passing interest in physics. Social scientists were really a joy to teach. They were interested in the material, the historical context and particularly the differences between astronomy, 'movie astronomy' and astrology. There's more than enough historical and current relevance to statistics to pique their interest, but you'll have to point it out.

I think the main difference is likely to be that sociological students are more used to questioning fundamental assumptions. I suppose this it because hard logic is a lot less useful when a large proportion of your reasoning is basedintuition. So be prepared to explain just about anything you consider "obvious", and to having your pedagogical skills tested to the limit.

I myself came to mathematics at university as an outsider; I found that my peers would simply accept most of what the teachers said, but I had a hard time adjusting to many of the viewpoints. Another thing I found difficult was spotting what it was I was supposed to learn - in the first years I would work hard on applying the major theorems to all exercises, and it was not until after my bachelor that one of my toturs exclaimed, with some exasperation: "Why don't you use the techniques that you have been shown in the proofs, like you are supposed to!?" - So the second thing you will need to do is, point out explicitly what you expect your students to learn.

Many people made good points about motivation (explain why it matters---with examples they can relate to.. Perhaps the early studies showing coffee was bad vs today's that show it increases longevity (removing the cohort that smoke and drank:))

The examples don't have to all be real, but they need to motivate:1) why being careful and not just dataming and publishing matters2) how to sensibly use good tools. They won't care about proofs or the central mean theorem don't bother3) illustrate good and bad tech

I would pick a book 'statistics for social sciences' or something like that, and see how that differs from your 'hard science' approach.
Once passed the basics (combinations/permutations/Baysian/...), the most important think to know is what exactly you try to achieve with the t,z and chi-test and how you can game these to still come out with the hypothesis you wanted;-)

If the OP thinks this is somehow different in social sciences vs the "hard" sciences, he is badly mistaken. In fact he might broaden his horizons a little to learn how to handle those experimental designs where you have no perfect control group since you can't just go out and give people cancer just to test in the real world, nor kill them just to autopsy them after the experiment has run its course.

He might end up losing some of his elitist attitude before the course is over. It would be better if lost the attitude ahead of time, and approached the experience like he was at least teaching the same species.

Indeed. I teach statistics to mathematicians, biologists, psychologists and social scientists and I would say the social scientists 'get' the principles of statistics better than the 'hard' scientists do. The main reason is that soft scientists (which is a horrible term) can think about uncertainty and its consequences, whereas hard scientists (mathematicians included) are unhappy if they don't have a yes/no answer to a question. Obviously this is a generalisation but it may inform your approach to teaching.

Also, statistics is not 'just math'. I know this because I can do statistics but I can't do math(s) any more.:-)

If you don't understand statistics you simply cannot work in the Social Sciences. Ever. You are not allowed to do the experiments necessary to isolate variables properly, and even if some sociopath (for example) traumatized three groups of ten people exactly the same way, and tried three different forms of psychological treatment on them to see what happens you'd run into the fact that all 30 of victims are individuals who will respond to each treatment differently.

Which means RL Psychologists are stuck doing a sophisticated study of people who just happened to get traumatized, and then chose a course of treatment; and the only way to get useful data from that is do lots of statistics. But not too much statistics or you risk over-fitting.

OTOH you can be a perfectly good chemist without understanding the difference between correlation and causation.

I've mentioned this before, but at a meeting of the British Association my supervisor in psychology, a statistics expert, presented a graph of the size estimates of Pluto and predicted a date for it to disappear.

Then he presented the calculations used to arrive at each size estimate from observation and showed how the published results had all been placed at the very top end of the (decreasing with time) range - because there was a strong desire to have Pluto larger than it was measured to be. When a line w

I'm sorry, am I misreading or are you saying statistics is a "soft science"?
If you're that confused about things, then just go to the textbook, and teach one chapter a week.

I understood the summary to mean that the OP is teaching a statistics course to soft science students (those who are majoring in social science and phychology), and not that (s)he considers statistics to be a soft science.

The OP says he normally teaches hard sciences to students with a major in a hard science meaning that they are more likely prepared for the learning of hard sciences. Because of some staffing issues the OP now must teach his hard science classes to students with a major in soft sciences, thus previous classes may not have fully prepared them for a hard science class.Because of this the OP is asking how to mold his teaching strategy to better target those soft science majors.

I am comfortable with the distinction between "hard" sciences where it is often possible to use the scientific method without reliance on statistical analysis of results, and "soft" sciences, where statistical analysis is critical to determining the findings of most experiments.

But I do not understand at all how mathematics can be considered any kind of science within this context.

Mathematics is not based on experimentation; empiricism has no place in its derivation. Every branch of mathematics is based

I have a "theory" about why we are seeing math referred to as a science more and more these days. Once you've bought into a naive epistemology of " if it isn't science, it's crap [wikipedia.org]", which a lot of not very thoughtful people have done, your choices are to either claim that math is science or to abandon it.

Computer science only gets called a hard science because it's encountered so close to Software Engineering. Literary deconstructionism is a harder science than Software Engineering the way it's currently studied.

Citing a specific example of hard science that is encompassed within a science does not prove the encompassing science itself is hard science. (notice I didn't say anything about statistics...just as the OP didn't say statistics was a soft science).

I think that professor was me. I am from Trinidad but my parents were from Nigeria where I am living now. I would love to get back in touch and have a beer with you. If you could please send me a small advance for travel along with your SSN and a passport photo I can begin to make arrangements.